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#pragma once

#include <pcl/common/centroid.h>
#include <pcl/features/feature.h>

namespace iris
{
namespace pcl_
{
/** \brief Compute the Least-Squares plane fit for a given set of points, and return the estimated plane
    * parameters together with the surface curvature.
    * \param cloud the input point cloud
    * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
    * \param curvature the estimated surface curvature as a measure of
    * \f[
    * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
    * \f]
    * \ingroup features
    */
template <typename PointT>
inline bool
computePointNormal(const pcl::PointCloud<PointT>& cloud,
    Eigen::Vector4f& plane_parameters, float& curvature)
{
  // Placeholder for the 3x3 covariance matrix at each surface patch
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
  // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
  Eigen::Vector4f xyz_centroid;

  if (cloud.size() < 3 || computeMeanAndCovarianceMatrix(cloud, covariance_matrix, xyz_centroid) == 0) {
    plane_parameters.setConstant(std::numeric_limits<float>::quiet_NaN());
    curvature = std::numeric_limits<float>::quiet_NaN();
    return false;
  }

  // Get the plane normal and surface curvature
  pcl::solvePlaneParameters(covariance_matrix, xyz_centroid, plane_parameters, curvature);
  return true;
}

/** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
    * and return the estimated plane parameters together with the surface curvature.
    * \param cloud the input point cloud
    * \param indices the point cloud indices that need to be used
    * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
    * \param curvature the estimated surface curvature as a measure of
    * \f[
    * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
    * \f]
    * \ingroup features
    */
template <typename PointT>
inline bool
computePointNormal(const pcl::PointCloud<PointT>& cloud, const std::vector<int>& indices,
    Eigen::Vector4f& plane_parameters, float& curvature)
{
  // Placeholder for the 3x3 covariance matrix at each surface patch
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
  // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
  Eigen::Vector4f xyz_centroid;
  if (indices.size() < 3 || computeMeanAndCovarianceMatrix(cloud, indices, covariance_matrix, xyz_centroid) == 0) {
    plane_parameters.setConstant(std::numeric_limits<float>::quiet_NaN());
    curvature = std::numeric_limits<float>::quiet_NaN();
    return false;
  }
  // Get the plane normal and surface curvature
  pcl::solvePlaneParameters(covariance_matrix, xyz_centroid, plane_parameters, curvature);
  return true;
}

/** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
    * \param point a given point
    * \param vp_x the X coordinate of the viewpoint
    * \param vp_y the X coordinate of the viewpoint
    * \param vp_z the X coordinate of the viewpoint
    * \param normal the plane normal to be flipped
    * \ingroup features
    */
template <typename PointT, typename Scalar>
inline void
flipNormalTowardsViewpoint(const PointT& point, float vp_x, float vp_y, float vp_z,
    Eigen::Matrix<Scalar, 4, 1>& normal)
{
  Eigen::Matrix<Scalar, 4, 1> vp(vp_x - point.x, vp_y - point.y, vp_z - point.z, 0);

  // Dot product between the (viewpoint - point) and the plane normal
  float cos_theta = vp.dot(normal);

  // Flip the plane normal
  if (cos_theta < 0) {
    normal *= -1;
    normal[3] = 0.0f;
    // Hessian form (D = nc . p_plane (centroid here) + p)
    normal[3] = -1 * normal.dot(point.getVector4fMap());
  }
}

/** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
    * \param point a given point
    * \param vp_x the X coordinate of the viewpoint
    * \param vp_y the X coordinate of the viewpoint
    * \param vp_z the X coordinate of the viewpoint
    * \param normal the plane normal to be flipped
    * \ingroup features
    */
template <typename PointT, typename Scalar>
inline void
flipNormalTowardsViewpoint(const PointT& point, float vp_x, float vp_y, float vp_z,
    Eigen::Matrix<Scalar, 3, 1>& normal)
{
  Eigen::Matrix<Scalar, 3, 1> vp(vp_x - point.x, vp_y - point.y, vp_z - point.z);

  // Flip the plane normal
  if (vp.dot(normal) < 0)
    normal *= -1;
}

/** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
    * \param point a given point
    * \param vp_x the X coordinate of the viewpoint
    * \param vp_y the X coordinate of the viewpoint
    * \param vp_z the X coordinate of the viewpoint
    * \param nx the resultant X component of the plane normal
    * \param ny the resultant Y component of the plane normal
    * \param nz the resultant Z component of the plane normal
    * \ingroup features
    */
template <typename PointT>
inline void
flipNormalTowardsViewpoint(const PointT& point, float vp_x, float vp_y, float vp_z,
    float& nx, float& ny, float& nz)
{
  // See if we need to flip any plane normals
  vp_x -= point.x;
  vp_y -= point.y;
  vp_z -= point.z;

  // Dot product between the (viewpoint - point) and the plane normal
  float cos_theta = (vp_x * nx + vp_y * ny + vp_z * nz);

  // Flip the plane normal
  if (cos_theta < 0) {
    nx *= -1;
    ny *= -1;
    nz *= -1;
  }
}

/** \brief NormalEstimation estimates local surface properties (surface normals and curvatures)at each
    * 3D point. If PointOutT is specified as pcl::Normal, the normal is stored in the first 3 components (0-2),
    * and the curvature is stored in component 3.
    *
    * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
    * \ref NormalEstimationOMP for a parallel implementation.
    * \author Radu B. Rusu
    * \ingroup features
    */
template <typename PointInT, typename PointOutT>
class NormalEstimation : public pcl::Feature<PointInT, PointOutT>
{
public:
  typedef boost::shared_ptr<NormalEstimation<PointInT, PointOutT>> Ptr;
  typedef boost::shared_ptr<const NormalEstimation<PointInT, PointOutT>> ConstPtr;
  using pcl::Feature<PointInT, PointOutT>::feature_name_;
  using pcl::Feature<PointInT, PointOutT>::getClassName;
  using pcl::Feature<PointInT, PointOutT>::indices_;
  using pcl::Feature<PointInT, PointOutT>::input_;
  using pcl::Feature<PointInT, PointOutT>::surface_;
  using pcl::Feature<PointInT, PointOutT>::k_;
  using pcl::Feature<PointInT, PointOutT>::search_radius_;
  using pcl::Feature<PointInT, PointOutT>::search_parameter_;

  typedef typename pcl::Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
  typedef typename pcl::Feature<PointInT, PointOutT>::PointCloudConstPtr PointCloudConstPtr;

  /** \brief Empty constructor. */
  NormalEstimation()
      : vpx_(0), vpy_(0), vpz_(0), covariance_matrix_(), xyz_centroid_(), use_sensor_origin_(true)
  {
    feature_name_ = "NormalEstimation";
  };

  /** \brief Empty destructor */
  virtual ~NormalEstimation() {}

  /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
        * and return the estimated plane parameters together with the surface curvature.
        * \param cloud the input point cloud
        * \param indices the point cloud indices that need to be used
        * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
        * \param curvature the estimated surface curvature as a measure of
        * \f[
        * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
        * \f]
        */
  inline bool
  computePointNormal(const pcl::PointCloud<PointInT>& cloud, const std::vector<int>& indices,
      Eigen::Vector4f& plane_parameters, float& curvature)
  {
    if (indices.size() < 3 || computeMeanAndCovarianceMatrix(cloud, indices, covariance_matrix_, xyz_centroid_) == 0) {
      plane_parameters.setConstant(std::numeric_limits<float>::quiet_NaN());
      curvature = std::numeric_limits<float>::quiet_NaN();
      return false;
    }

    // Get the plane normal and surface curvature
    pcl::solvePlaneParameters(covariance_matrix_, xyz_centroid_, plane_parameters, curvature);
    return true;
  }

  /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
        * and return the estimated plane parameters together with the surface curvature.
        * \param cloud the input point cloud
        * \param indices the point cloud indices that need to be used
        * \param nx the resultant X component of the plane normal
        * \param ny the resultant Y component of the plane normal
        * \param nz the resultant Z component of the plane normal
        * \param curvature the estimated surface curvature as a measure of
        * \f[
        * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
        * \f]
        */
  inline bool
  computePointNormal(const pcl::PointCloud<PointInT>& cloud, const std::vector<int>& indices,
      float& nx, float& ny, float& nz, float& curvature)
  {
    if (indices.size() < 3 || computeMeanAndCovarianceMatrix(cloud, indices, covariance_matrix_, xyz_centroid_) == 0) {
      nx = ny = nz = curvature = std::numeric_limits<float>::quiet_NaN();
      return false;
    }

    // Get the plane normal and surface curvature
    pcl::solvePlaneParameters(covariance_matrix_, nx, ny, nz, curvature);
    return true;
  }

  /** \brief Provide a pointer to the input dataset
        * \param cloud the const boost shared pointer to a PointCloud message
        */
  virtual inline void
  setInputCloud(const PointCloudConstPtr& cloud)
  {
    input_ = cloud;
    if (use_sensor_origin_) {
      vpx_ = input_->sensor_origin_.coeff(0);
      vpy_ = input_->sensor_origin_.coeff(1);
      vpz_ = input_->sensor_origin_.coeff(2);
    }
  }

  /** \brief Set the viewpoint.
        * \param vpx the X coordinate of the viewpoint
        * \param vpy the Y coordinate of the viewpoint
        * \param vpz the Z coordinate of the viewpoint
        */
  inline void
  setViewPoint(float vpx, float vpy, float vpz)
  {
    vpx_ = vpx;
    vpy_ = vpy;
    vpz_ = vpz;
    use_sensor_origin_ = false;
  }

  /** \brief Get the viewpoint.
        * \param [out] vpx x-coordinate of the view point
        * \param [out] vpy y-coordinate of the view point
        * \param [out] vpz z-coordinate of the view point
        * \note this method returns the currently used viewpoint for normal flipping.
        * If the viewpoint is set manually using the setViewPoint method, this method will return the set view point coordinates.
        * If an input cloud is set, it will return the sensor origin otherwise it will return the origin (0, 0, 0)
        */
  inline void
  getViewPoint(float& vpx, float& vpy, float& vpz)
  {
    vpx = vpx_;
    vpy = vpy_;
    vpz = vpz_;
  }

  /** \brief sets whether the sensor origin or a user given viewpoint should be used. After this method, the 
        * normal estimation method uses the sensor origin of the input cloud.
        * to use a user defined view point, use the method setViewPoint
        */
  inline void
  useSensorOriginAsViewPoint()
  {
    use_sensor_origin_ = true;
    if (input_) {
      vpx_ = input_->sensor_origin_.coeff(0);
      vpy_ = input_->sensor_origin_.coeff(1);
      vpz_ = input_->sensor_origin_.coeff(2);
    } else {
      vpx_ = 0;
      vpy_ = 0;
      vpz_ = 0;
    }
  }

protected:
  /** \brief Estimate normals for all points given in <setInputCloud (), setIndices ()> using the surface in
        * setSearchSurface () and the spatial locator in setSearchMethod ()
        * \note In situations where not enough neighbors are found, the normal and curvature values are set to NaN.
        * \param output the resultant point cloud model dataset that contains surface normals and curvatures
        */
  void
  computeFeature(PointCloudOut& output);

  /** \brief Values describing the viewpoint ("pinhole" camera model assumed). For per point viewpoints, inherit
        * from NormalEstimation and provide your own computeFeature (). By default, the viewpoint is set to 0,0,0. */
  float vpx_, vpy_, vpz_;

  /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
  EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;

  /** \brief 16-bytes aligned placeholder for the XYZ centroid of a surface patch. */
  Eigen::Vector4f xyz_centroid_;

  /** whether the sensor origin of the input cloud or a user given viewpoint should be used.*/
  bool use_sensor_origin_;

public:
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};

}  // namespace pcl_
}  // namespace iris